{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-08-12T05:28:06.464746Z",
     "start_time": "2025-08-12T05:28:02.918796Z"
    }
   },
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.decomposition import PCA\n",
    "import jieba\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 1、含有字符串的特征的特征提取",
   "id": "e0e8b43efad24b5f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T05:29:12.296785Z",
     "start_time": "2025-08-12T05:29:12.288090Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def dictvec():\n",
    "    \"\"\"\n",
    "    字典数据提取\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    # 实例化\n",
    "    dict1 = DictVectorizer(sparse=False) # sparse=True 稀疏矩阵\n",
    "\n",
    "    # 每个样本都是一个字典\n",
    "    data = dict1.fit_transform([{'city': '北京', 'temperature': 100},\n",
    "                               {'city': '上海', 'temperature': 60},\n",
    "                               {'city': '深圳', 'temperature': 30}])\n",
    "\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(dict1.get_feature_names_out())    # 获取特征名字\n",
    "    print('-' * 50)\n",
    "    print(dict1.inverse_transform(data))    # 还原\n",
    "\n",
    "dictvec()\n"
   ],
   "id": "64629a14166cbed5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.   1.   0. 100.]\n",
      " [  1.   0.   0.  60.]\n",
      " [  0.   0.   1.  30.]]\n",
      "--------------------------------------------------\n",
      "['city=上海' 'city=北京' 'city=深圳' 'temperature']\n",
      "--------------------------------------------------\n",
      "[{'city=北京': 1.0, 'temperature': 100.0}, {'city=上海': 1.0, 'temperature': 60.0}, {'city=深圳': 1.0, 'temperature': 30.0}]\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 2、英文文本转换为数值类型",
   "id": "ebc8bddf5697776"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T05:48:13.279743Z",
     "start_time": "2025-08-12T05:48:13.269369Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def couvec():\n",
    "    \"\"\"\n",
    "    英文文本转换为数值类型\n",
    "    max_df, min_df整数: 指每个词的所有文档词频数不小于最小值，出现该词的文档数目小于等于max_df\n",
    "    max_df, min_df小数: 0-1，某个词的出现的次数/所有文档数量\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    vector = CountVectorizer(min_df=2)  # 分词并统计词频，词频少于2的词会被过滤掉，不考虑单个字母\n",
    "\n",
    "    res = vector.fit_transform(['life is short, you need python',\n",
    "                               'life is too long, you need sklearn',\n",
    "                                'life is not short, you dislike python'])\n",
    "\n",
    "    print(vector.get_feature_names_out())   # 获取特征名字\n",
    "    print('-' * 50)\n",
    "    print(res)\n",
    "    print('-' * 50)\n",
    "    print(type(res))\n",
    "    print('-' * 50)\n",
    "    print(res.toarray())    # 稀疏矩阵转换为数组\n",
    "    print('-' * 50)\n",
    "    print(vector.inverse_transform(res))\n",
    "\n",
    "couvec()"
   ],
   "id": "42ef37df79b374d2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['is' 'life' 'need' 'python' 'short' 'you']\n",
      "--------------------------------------------------\n",
      "<Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 15 stored elements and shape (3, 6)>\n",
      "  Coords\tValues\n",
      "  (0, 1)\t1\n",
      "  (0, 0)\t1\n",
      "  (0, 4)\t1\n",
      "  (0, 5)\t1\n",
      "  (0, 2)\t1\n",
      "  (0, 3)\t1\n",
      "  (1, 1)\t1\n",
      "  (1, 0)\t1\n",
      "  (1, 5)\t1\n",
      "  (1, 2)\t1\n",
      "  (2, 1)\t1\n",
      "  (2, 0)\t1\n",
      "  (2, 4)\t1\n",
      "  (2, 5)\t1\n",
      "  (2, 3)\t1\n",
      "--------------------------------------------------\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n",
      "[[1 1 1 1 1 1]\n",
      " [1 1 1 0 0 1]\n",
      " [1 1 0 1 1 1]]\n",
      "--------------------------------------------------\n",
      "[array(['life', 'is', 'short', 'you', 'need', 'python'], dtype='<U6'), array(['life', 'is', 'you', 'need'], dtype='<U6'), array(['life', 'is', 'short', 'you', 'python'], dtype='<U6')]\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 3、中文文本转换为数值类型",
   "id": "83b20db8bf4035cc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T05:54:24.873509Z",
     "start_time": "2025-08-12T05:54:24.865642Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def countvec():\n",
    "    \"\"\"\n",
    "    CountVectorizer只能按照，和空格分词，无法满足中文分词\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    vector = CountVectorizer()\n",
    "\n",
    "    data = vector.fit_transform(['我 爱 北京 天安门',\n",
    "                               '天安门 上 太阳升'])\n",
    "\n",
    "    print(vector.get_feature_names_out())   # 获取特征名字\n",
    "    print('-' * 50)\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(type(data))\n",
    "    print('-' * 50)\n",
    "\n",
    "countvec()"
   ],
   "id": "42a7df0b500c68d6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['北京' '天安门' '太阳升']\n",
      "--------------------------------------------------\n",
      "<Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 4 stored elements and shape (2, 3)>\n",
      "  Coords\tValues\n",
      "  (0, 0)\t1\n",
      "  (0, 1)\t1\n",
      "  (1, 1)\t1\n",
      "  (1, 2)\t1\n",
      "--------------------------------------------------\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T06:08:54.121522Z",
     "start_time": "2025-08-12T06:08:54.105944Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def cutword():\n",
    "    \"\"\"\n",
    "    中文分词\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    con1 = jieba.cut(\"今天很残酷，明天更残酷，后天很美好，但绝大部分是死在明天晚上，所以每个人不要放弃今天\")\n",
    "    con2 = jieba.cut_for_search(\"我们看到的从很远星系来的光是在几百万年之前出发的，这样当我们看到宇宙时，我们是在看它的过去\")\n",
    "    con3 = jieba.cut(\"如果只用一种方式了解某样事物，你就不会真正了解它。了解事物真正含义的秘密取决于如何将其与我们所了解的事物相联系\")\n",
    "\n",
    "    print(type(con1))   # 迭代器类型\n",
    "    print('-' * 50)\n",
    "    content1 = list(con1)\n",
    "    content2 = list(con2)\n",
    "    content3 = list(con3)\n",
    "    print(content1)\n",
    "    print('-' * 50)\n",
    "    print(content2)\n",
    "    print('-' * 50)\n",
    "    print(content3)\n",
    "    print('-' * 50)\n",
    "    c1 = ' '.join(content1) # 将列表转换为字符串\n",
    "    c2 = ' '.join(content2)\n",
    "    c3 = ' '.join(content3)\n",
    "\n",
    "    return c1, c2, c3\n",
    "\n",
    "def hanzivec():\n",
    "    \"\"\"\n",
    "    中文文本转换为数值类型\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    c1, c2, c3 = cutword()\n",
    "    print('-' * 50)\n",
    "    print(c1)\n",
    "    print('-' * 50)\n",
    "    print(c2)\n",
    "    print('-' * 50)\n",
    "    print(c3)\n",
    "    print('-' * 50)\n",
    "    vector = CountVectorizer()\n",
    "    data = vector.fit_transform([c1, c2, c3])\n",
    "    print(vector.get_feature_names_out())\n",
    "    print('-' * 50)\n",
    "    # print(data)\n",
    "    print('-' * 50)\n",
    "    print(data.toarray())\n",
    "    print('-' * 50)\n",
    "    print(vector.inverse_transform(data))\n",
    "\n",
    "hanzivec()"
   ],
   "id": "3936e26930c2ea71",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'generator'>\n",
      "--------------------------------------------------\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天']\n",
      "--------------------------------------------------\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是', '光是在', '几百', '百万', '万年', '几百万', '百万年', '几百万年', '之前', '出发', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去']\n",
      "--------------------------------------------------\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系']\n",
      "--------------------------------------------------\n",
      "--------------------------------------------------\n",
      "今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天\n",
      "--------------------------------------------------\n",
      "我们 看到 的 从 很 远 星系 来 的 光是 光是在 几百 百万 万年 几百万 百万年 几百万年 之前 出发 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去\n",
      "--------------------------------------------------\n",
      "如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系\n",
      "--------------------------------------------------\n",
      "['一种' '万年' '不会' '不要' '之前' '了解' '事物' '今天' '光是' '光是在' '几百' '几百万' '几百万年' '出发'\n",
      " '取决于' '只用' '后天' '含义' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上'\n",
      " '某样' '残酷' '每个' '百万' '百万年' '看到' '真正' '秘密' '绝大部分' '美好' '联系' '过去' '这样']\n",
      "--------------------------------------------------\n",
      "--------------------------------------------------\n",
      "[[0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 2 0 1 0 2 1 0 0 0 0 0\n",
      "  1 1 0 0 0]\n",
      " [0 1 0 0 1 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 3 0 0 0 0 1 0 0 0 0 1 1 2 0 0\n",
      "  0 0 0 1 1]\n",
      " [1 0 1 0 0 4 3 0 0 0 0 0 0 0 1 1 0 1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 2 1\n",
      "  0 0 1 0 0]]\n",
      "--------------------------------------------------\n",
      "[array(['今天', '残酷', '明天', '后天', '美好', '绝大部分', '晚上', '所以', '每个', '不要', '放弃'],\n",
      "      dtype='<U4'), array(['我们', '看到', '星系', '光是', '光是在', '几百', '百万', '万年', '几百万', '百万年',\n",
      "       '几百万年', '之前', '出发', '这样', '宇宙', '过去'], dtype='<U4'), array(['我们', '如果', '只用', '一种', '方式', '了解', '某样', '事物', '不会', '真正', '含义',\n",
      "       '秘密', '取决于', '如何', '联系'], dtype='<U4')]\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 4、TF-IDF",
   "id": "4e2dc22cdf8f6f8a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T06:14:00.947375Z",
     "start_time": "2025-08-12T06:14:00.923514Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def tfidfvec():\n",
    "    \"\"\"\n",
    "    TF:词频\n",
    "    IDF：逆文档频率 log(总文档数量/该词出现的文档数量)\n",
    "    以TF*IDF来评估重要性\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    c1, c2, c3 = cutword()\n",
    "    tf = TfidfVectorizer(smooth_idf=True)   # 平滑参数，防止IDF为0(生僻词可能在文档中未出现)\n",
    "\n",
    "    data = tf.fit_transform([c1, c2, c3])\n",
    "    print(tf.get_feature_names_out())\n",
    "    print('-' * 50)\n",
    "    print(type(data))\n",
    "    print('-' * 50)\n",
    "    print(data.toarray())\n",
    "    print('-' * 50)\n",
    "    print(tf.inverse_transform(data))\n",
    "\n",
    "    return None\n",
    "\n",
    "tfidfvec()"
   ],
   "id": "e08a5407616c59c7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'generator'>\n",
      "--------------------------------------------------\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天']\n",
      "--------------------------------------------------\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是', '光是在', '几百', '百万', '万年', '几百万', '百万年', '几百万年', '之前', '出发', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去']\n",
      "--------------------------------------------------\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系']\n",
      "--------------------------------------------------\n",
      "['一种' '万年' '不会' '不要' '之前' '了解' '事物' '今天' '光是' '光是在' '几百' '几百万' '几百万年' '出发'\n",
      " '取决于' '只用' '后天' '含义' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上'\n",
      " '某样' '残酷' '每个' '百万' '百万年' '看到' '真正' '秘密' '绝大部分' '美好' '联系' '过去' '这样']\n",
      "--------------------------------------------------\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n",
      "[[0.         0.         0.         0.2236068  0.         0.\n",
      "  0.         0.4472136  0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.2236068  0.\n",
      "  0.         0.         0.         0.         0.2236068  0.2236068\n",
      "  0.         0.4472136  0.         0.2236068  0.         0.4472136\n",
      "  0.2236068  0.         0.         0.         0.         0.\n",
      "  0.2236068  0.2236068  0.         0.         0.        ]\n",
      " [0.         0.20758867 0.         0.         0.20758867 0.\n",
      "  0.         0.         0.20758867 0.20758867 0.20758867 0.20758867\n",
      "  0.20758867 0.20758867 0.         0.         0.         0.\n",
      "  0.         0.         0.20758867 0.4736296  0.         0.\n",
      "  0.         0.         0.20758867 0.         0.         0.\n",
      "  0.         0.20758867 0.20758867 0.41517734 0.         0.\n",
      "  0.         0.         0.         0.20758867 0.20758867]\n",
      " [0.15698297 0.         0.15698297 0.         0.         0.62793188\n",
      "  0.47094891 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.15698297 0.15698297 0.         0.15698297\n",
      "  0.15698297 0.15698297 0.         0.1193896  0.         0.\n",
      "  0.15698297 0.         0.         0.         0.15698297 0.\n",
      "  0.         0.         0.         0.         0.31396594 0.15698297\n",
      "  0.         0.         0.15698297 0.         0.        ]]\n",
      "--------------------------------------------------\n",
      "[array(['今天', '残酷', '明天', '后天', '美好', '绝大部分', '晚上', '所以', '每个', '不要', '放弃'],\n",
      "      dtype='<U4'), array(['我们', '看到', '星系', '光是', '光是在', '几百', '百万', '万年', '几百万', '百万年',\n",
      "       '几百万年', '之前', '出发', '这样', '宇宙', '过去'], dtype='<U4'), array(['我们', '如果', '只用', '一种', '方式', '了解', '某样', '事物', '不会', '真正', '含义',\n",
      "       '秘密', '取决于', '如何', '联系'], dtype='<U4')]\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 5、数据归一化",
   "id": "ab07398eee89e97d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T06:41:42.984921Z",
     "start_time": "2025-08-12T06:41:42.964894Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 公式 x' = (x-min(x))/(max(x)-min(x))   x'' = x' * (mx - mi) + mi   mi, mx为指定区间\n",
    "def mm():\n",
    "    \"\"\"\n",
    "    数据归一化\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    mm = MinMaxScaler(feature_range=(0, 1)) # 归一化范围\n",
    "    data = mm.fit_transform([[90, 2, 10, 40], [60, 4, 15, 25], [75, 3, 5, 35]])\n",
    "    print(data)\n",
    "    return  None\n",
    "\n",
    "mm()"
   ],
   "id": "7996b9c25c1e5833",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.         0.5        1.        ]\n",
      " [0.         1.         1.         0.        ]\n",
      " [0.5        0.5        0.         0.66666667]]\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 6、数据标准化",
   "id": "428edd52519a2118"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T06:59:17.843936Z",
     "start_time": "2025-08-12T06:59:17.831635Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 公式 x' = (x-mean(x))/std(x)\n",
    "def stand():\n",
    "    \"\"\"\n",
    "    数据标准化\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    stand = StandardScaler()\n",
    "    data = stand.fit_transform([[1., -1., 3.], [2., 4., 2.],[4., 6., -1.]])\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(stand.mean_)  # 均值\n",
    "    print('-' * 50)\n",
    "    print(stand.var_)   # 方差\n",
    "    print('-' * 50)\n",
    "    print(stand.n_samples_seen_)    # 样本数\n",
    "    return  None\n",
    "\n",
    "stand()"
   ],
   "id": "b027d9bac97e720a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.06904497 -1.35873244  0.98058068]\n",
      " [-0.26726124  0.33968311  0.39223227]\n",
      " [ 1.33630621  1.01904933 -1.37281295]]\n",
      "--------------------------------------------------\n",
      "[2.33333333 3.         1.33333333]\n",
      "--------------------------------------------------\n",
      "[1.55555556 8.66666667 2.88888889]\n",
      "--------------------------------------------------\n",
      "3\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 7、缺失值处理",
   "id": "2cd03e175e350163"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T07:06:11.351995Z",
     "start_time": "2025-08-12T07:06:11.341403Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def im():\n",
    "    \"\"\"\n",
    "    缺失值处理\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    im = SimpleImputer(missing_values=np.nan, strategy='mean')  # 用均值处理\n",
    "    data = im.fit_transform([[1, 2], [np.nan, 3], [7, 6], [3, 2]])\n",
    "    print(data)\n",
    "    return None\n",
    "\n",
    "im()\n"
   ],
   "id": "3d98f8b4821f9806",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         2.        ]\n",
      " [3.66666667 3.        ]\n",
      " [7.         6.        ]\n",
      " [3.         2.        ]]\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 8、降维",
   "id": "24e5e589efdc4192"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T08:02:01.906803Z",
     "start_time": "2025-08-12T08:02:01.865436Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def var():\n",
    "    \"\"\"\n",
    "    特征选择-删除低方差的特征\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    # 默认只删除方差为0，threshold是方差阈值，删除比这个值小的特征\n",
    "    var = VarianceThreshold(threshold=0.6)\n",
    "    data = var.fit_transform([[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]])\n",
    "    print(data)\n",
    "    print('The support is %s' % var.get_support(True))  # 返回特征选择结果\n",
    "    return  None\n",
    "\n",
    "var()"
   ],
   "id": "2114f6758d4363d7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [4]\n",
      " [1]]\n",
      "The support is [2]\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T08:21:22.640147Z",
     "start_time": "2025-08-12T08:21:22.480983Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 作代数运算降维\n",
    "def pca():\n",
    "    \"\"\"\n",
    "    主成分分析进行特征降维\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    print(np.var(np.array([[2, 8, 4, 5], [6, 3, 0, 8], [5, 4, 9, 1]]), axis=0).sum())\n",
    "    print('-' * 50)\n",
    "    pca = PCA(n_components=0.9) # 保留90%的方差\n",
    "    data = pca.fit_transform([[2, 8, 4, 5], [6, 3, 0, 8], [5, 4, 9, 1]])\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(type(data))\n",
    "    print(np.var(data, axis=0).sum())\n",
    "    print('-' * 50)\n",
    "    print(pca.explained_variance_ratio_)\n",
    "    print(pca.explained_variance_ratio_.sum())\n",
    "    return None\n",
    "\n",
    "pca()"
   ],
   "id": "e959007bc94a7fd2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.333333333333336\n",
      "--------------------------------------------------\n",
      "[[-1.28620952e-15  3.82970843e+00]\n",
      " [-5.74456265e+00 -1.91485422e+00]\n",
      " [ 5.74456265e+00 -1.91485422e+00]]\n",
      "--------------------------------------------------\n",
      "<class 'numpy.ndarray'>\n",
      "29.333333333333332\n",
      "--------------------------------------------------\n",
      "[0.75 0.25]\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-12T08:28:20.532782Z",
     "start_time": "2025-08-12T08:28:17.600497Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "x = np.random.rand(10000)\n",
    "t = np.arange(len(x))   # 时间\n",
    "plt.plot(t,x,'g.',label='Uniform Distribution')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "id": "6626aeb14cbde17c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 23
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
